Lifetime Prediction Analysis of Proton Exchange Membrane Fuel Cells Based on Empirical Mode Decomposition—Temporal Convolutional Network

Lifetime Prediction Analysis of Proton Exchange Membrane Fuel Cells Based on Empirical Mode Decomposition—Temporal Convolutional Network

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essen...

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Bibliographic Details
Main Authors: Chao Zheng, Changqing Du, Jiaming Zhang, Yiming Zhang, Jun Shen, Jiaxin Huang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Batteries
Subjects:
PEMFC
EMD-TCN
life prediction
deep learning
Online Access:https://www.mdpi.com/2313-0105/11/6/226
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